| Time (hours) | Cumulative Release (%) | Released Amount (mg) for 500 mg dose |
|---|---|---|
| 1 | 18 | 90 |
| 2 | 30 | 150 |
| 4 | 46 | 230 |
| 6 | 59 | 295 |
| 8 | 70 | 350 |
| 12 | 86 | 430 |
Use this sample to verify input formatting and inspect how the model ranking behaves with a progressive cumulative release profile.
Zero-order: Q(t) = k0 × t + b — release rises linearly with time.
First-order: ln(100 - Q) = a - k1 × t — remaining drug declines exponentially.
Higuchi: Q(t) = kH × √t + b — diffusion-controlled release scales with square-root time.
Hixson-Crowell: (1 - Q/100)^(1/3) = a - kHC × t — accounts for dimensional change during dissolution.
Korsmeyer-Peppas: Q/100 = kKP × t^n — mechanism indicator using the release exponent n.
Weibull: Q/100 = 1 - exp(-(t/α)^β) — flexible empirical model for complex release curves.
This calculator transforms the data, fits each model with linear regression on its standard linearized form, then ranks models using R² and RMSE.
- Enter the total drug amount for the tested unit or formulation.
- Choose the time and mass labels you want shown in the report.
- Paste paired time values and cumulative release percentages in matching order.
- Set a target prediction time and choose best-fit or a specific model.
- Click Calculate Kinetics to generate fitted constants and rankings.
- Review the model comparison table, best-fit summary, and observed-versus-predicted trend.
- Export the current report to CSV or PDF when needed.
1) What does this calculator measure?
It compares experimental release data against common kinetic models. The output helps estimate rate constants, release mechanisms, and how well each model matches the observed profile.
2) Why are cumulative percentages required?
Most standard release models are compared on normalized cumulative release fractions. Percentages make model ranking easier and let the calculator estimate released mass from the entered total dose.
3) Which model should I trust most?
Begin with the best R² and lowest RMSE, then verify whether the model also makes physical sense for the formulation, matrix type, geometry, and expected mechanism.
4) What does the Korsmeyer-Peppas exponent mean?
The exponent n indicates the likely transport mechanism. Its interpretation depends on dosage form geometry, so use it with the correct slab, cylinder, or sphere framework.
5) Can I use non-monotonic release data?
You can enter it, but inconsistent cumulative values often reduce fit quality. Experimental noise, sampling issues, or assay drift may distort model ranking and predictions.
6) Why does one model not appear sometimes?
Some models need valid transformed values. For example, logarithmic models cannot use zero time in certain steps, and the Peppas model needs early fractional release data below 60%.
7) Is this suitable for regulatory decisions?
It is useful for screening, development, and interpretation. Formal regulatory submissions usually require validated methods, controlled study design, and broader statistical justification.
8) What improves prediction reliability?
Use evenly spaced time points, accurate assay results, meaningful early and late sampling, and enough observations to represent the release curve across the intended duration.